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A Cardiovascular Disease Detection System using machine learning 5

A Cardiovascular Disease Detection System using Machine Lear...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Srikanth, V. Mahesh, T.R. Department of Computer Science and IT Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350398267

In recent times, the health sector has placed a greater emphasis on data comprehension through the utilization of data mining and collection technology. Multiple design standards indicate that medical practitioners make between 12 and 13% of incorrect prognostications. therefore, an automated method for disease prediction is required in order to improve the accuracy of health diagnoses and to raise assessments reliability. Many academics have placed an emphasis on accuracy rate throughout the construction of automated techniques. the automated system that was created to forecast heart disease will reduce the burden of computational work and the factors that raise the amount of computational work. the accuracy of the prediction is improved through the employment of the classification models, which retrieves and selects data. the information about heart disease that is readily available to the public yields contradictory outcomes when it is investigated. learning patterns involves both linear and non-linear examination of the underlying features. the prediction rates can be improved by Support vector regression that is linearly iterated (LlSVR) and classification using stacked monkey optimization (SMO). the computations were performed in MATLAB 2016b, and a number of evaluation techniques were used to assess the outcomes. the suggested model is 98.5 percent accurate, 98.8 percent precise, 99.5 percent recall, and 99.28 percent F-measure. the findings point to the possibility that the anticipated LISVR-SMO will function better than the approaches that are now being used and provide better trading. © 2022 IEEE.

关键词： Forecasting

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Deep learning-Based Sentiment Analysis for Social Media 5

Deep Learning-Based Sentiment Analysis for Social Media

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Wang, Zhe Liu, Ying Fang, Jie Li, Daxiang Center for Image and Information Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

Due to the continuous popularization of the Internet and mobile phones, people have gradually entered a participatory network era, and the rapid growth of social networks has caused an explosion of digital information content. It has turned online opinions, blogs, tweets and posts into highly valuable assets, allowing governments and businesses to gain insights from the data and make their strategies. Business organizations need to process and analyze these sentiments to investigate the data and gain business insights. In recent years, deep learning techniques have been very successful in performing sentiment analysis, which offers automatic feature extraction, rich representation capabilities and better performance compared with traditional feature-based techniques. the core idea is to extract complex features automatically from large amounts of data by building deep neural networks to generate up-to-date predictions. this paper reviews social media sentiment analysis methods based on deep learning. Firstly, it introduces the process of single-modal text sentiment analysis on social media. then it summarizes the multimodal sentiment analysis algorithms for social media, and divides the algorithm into feature layer fusion, decision layer fusion and linear regression model according to different fusion strategies. finally, the difficulties of social media sentiment analysis based on deep learning and future research directions are discussed. © 2022 ACM.

关键词： Sentiment analysis

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Research on Salient Object Detection Algorithm Based on Image Depth Information

Research on Salient Object Detection Algorithm Based on Imag...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Peijie Zeng Wei Zhou School of Artificial Intelligence Guangxi Minzu University Nanning China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

this paper proposes a novel RGBD segmentation network structure named AsymFusion-FPDNet, aiming to improve the accuracy of RGBD segmentation by fully utilizing the rich semantic information of RGB images. Although significant progress has been made in existing RGBD segmentation methods, most of them primarily focus on the utilization of depth image information and the fusion of RGB and depth information, while overlooking the semantic information in RGB images. therefore, based on the asymmetric multimodal structural network AsymFormer, we introduce dilated convolution blocks after the final downsampling operation of the RGB image feature extraction network. By gradually expanding the receptive field through dilated convolutions with different dilation factors, we extract more extensive feature information to enhance the representation capability of the network. Additionally, we incorporate a feature pyramid module and a CSPLayer_2Conv structure into the RGB image feature extraction network structure to improve the model's feature extraction ability in complex scenes, particularly by fusing spatial details and semantic information to enhance the model's comprehensive understanding and capture of targets. Experimental results show that AsymFusion-FPDNet achieves excellent performance on the NYUv2 dataset, with a MIoU of 56.91% and a PA of 79.78%, representing an improvement of 2.81% in MIoU and 1.29% in PA compared to the advanced AsymFormer model. this validates the effectiveness and advancement of the proposed model for RGBD segmentation tasks.

关键词： Image segmentation Visualization machine learning algorithms Convolution Semantics Object detection machine learning Transformer cores Feature extraction Transformers

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Leveraging Semi-Supervised learning to Enhance data mining for Image Classification under Limited Labeled data

Leveraging Semi-Supervised Learning to Enhance Data Mining f...

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Aoran Shen Minghao Dai Jiacheng Hu Yingbin Liang Shiru Wang Junliang Du University of Michigan Ann Arbor USA Columbia University New York USA Tulane University New Orleans USA Northeastern University Seattle USA Dartmouth College Hanover USA Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. this study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. the experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios.

关键词： Support vector machines Visualization Accuracy Semisupervised learning Feature extraction Classification algorithms Behavioral sciences data mining Convolutional neural networks Image classification

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Enhanced Approaches to Kingfisher Species Identification Using machine learning and Deep learning 5

Enhanced Approaches to Kingfisher Species Identification Usi...

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5th international conference on Advanced Research in Computing, ICARC 2025

作者： Vidanagamachchi, C.S. Chathuranga, W.G.D. Chathuranga, L.L.G. Jayathilake, K.T.A.S. Yeshani, R.B. Vidanagamachchi, S.M. University of Ruhuna Dept. of Computer Science Matara Sri Lanka University of Ruhuna Dept. of Zoology Matara Sri Lanka

ISBN: (纸本)9798331530983

this study addresses the critical challenge of bird species recognition within the domains of deep learning, emphasizing its significant ecological and conservation implications. Focusing on Sri Lanka's avian biodiversity, particularly the Kingfisher family, this research systematically analyzes bird identification methodologies. the methodology encompasses data collection, pre-processing, feature extraction, classification, and analysis of results. the study employs various deep learning models, including VGG16, VGG19, SVM, a custom CNN, and a hybrid CNN-SVM model. the custom CNN model, specifically optimized for kingfisher species identification, achieved a testing accuracy of 81.21%, outperforming traditional models. However, the hybrid CNN-SVM model demonstrated slightly lower testing accuracy at 79.35%. these findings highlight the effectiveness of the proposed deep learning models in identifying Kingfisher species, particularly when dealing with complex variations in plumage and environmental factors. the results indicate that the custom CNN model is highly reliable for ornithological applications, especially for nonspecialists. the research highlights the complexity of Kingfisher species identification and underscores the critical role of advanced machine learning techniques in addressing these challenges. the findings have significant implications for wildlife conservation, enabling enhanced monitoring and preservation of bird populations. Additionally, the results reveal the potential for developing a mobile application for bird species recognition, particularly for Kingfishers, addressing the lack of existing computer-based identification solutions. © 2025 IEEE.

关键词： Feature extraction

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Proceedings - 2022 IEEE international conference on Artificial Intelligence and Virtual Reality, AIVR 2022

Proceedings - 2022 IEEE International Conference on Artifici...

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5th IEEE international conference on Artificial Intelligence and Virtual Reality, AIVR 2022

ISBN: (纸本)9781665457255

the proceedings contain 49 papers. the topics discussed include: an end-to-end learning-based approach to 3D novel view style transfer;comparison of data encodings and machine learning architectures for user identification on arbitrary motion sequences;efficient and iterative training for high-performance light field synthesis;improving accessibility of elevation control in an immersive virtual environment;multimodal embodied conversational agents: a discussion of architectures, frameworks and modules for commercial applications;cumulative evidence for scene change detection and local map updates;empathizing with virtual agents: the effect of personification and general empathic tendencies;XR management training simulator supported by content-based scenario recommendation;re-enacting football matches in VR using virtual agents’ realistic behaviors;and capture and recognition of bead weaving activities using hand skeletal data and an LSTM deep neural network.

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Leveraging machine learning to Predict Disease Risk Levels from Multiple Eye Condition Indicators

Leveraging Machine Learning to Predict Disease Risk Levels f...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Mudumala Varnika Narayani Nunnaguppala Rohit Naga Ruthvika Durupudi Tejashwini Vadeghar Jyotsna C. Aiswariya Milan K. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

this paper presents a review of machine learning methodologies applied to disease risk evaluation, leveraging ocular condition indices derived from the RFMiD dataset. To enhance the performance of classification, a formal and structured mechanism of statistical analysis and also the method of model prediction was applied. data mining techniques which include Decision Trees, Random Forest, Naive Bayes, Gradient Boosting, and Stacking Classifiers were evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. For the cases of coarse models applied to healthcare XAI approaches such as SHAP and LIME were used to gain better interpretability. this paper points to the potential of machine learning for disease risk modelling and provides recommendations for generating accurate and easily-interpreted models in order to increase the diagnostic accuracy and, therefore, the quality of care for patients.

关键词： Analytical models Accuracy Computational modeling Medical services Predictive models Retina Real-time systems Bayes methods Wearable devices Diseases

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Use of Efficient machine learning Techniques in the Identification of Patients with Heart Diseases 2021

Use of Efficient Machine Learning Techniques in the Identifi...

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5th international conference on Information System and data mining (ICISDM)

作者： Ghosh, Pronab Azam, Sami Karim, Asif Jonkman, Mirjam Hasan, Md Zahid Daffodil Int Univ Dhaka Bangladesh Charles Darwin Univ Darwin NT Australia

ISBN: (纸本)9781450389549

Cardiovascular disease has become one of the world's major causes of death. Accurate and timely diagnosis is of crucial importance. We constructed an intelligent diagnostic framework for prediction of heart disease, using the Cleveland Heart disease dataset. We have used three machine learning approaches, Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest (RF) in combination with different sets of features. We have applied the three techniques to the full set of features, to a set of ten features selected by "Pearson's Correlation" technique and to a set of six features selected by the Relief algorithm. Results were evaluated based on accuracy, precision, sensitivity, and several other indices. the best results were obtained with the combination of the RF classifier and the features selected by Relief achieving an accuracy of 98.36%. this could even further be improved by employing a 5-fold Cross Validation (CV) approach, resulting in an accuracy of 99.337%.

关键词： Relief Pearson Correlations K-Nearest Neighbor Decision Tree and Random Forest

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Investigation of machine learning Algorithms for pattern recognition in Image Processing

Investigation of Machine Learning Algorithms for Pattern Rec...

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international conference on Inventive Research in Computing Applications (ICIRCA)

作者： Chennaiah Kate C. Kalpana Arvind Sharma Ajay Singh Yadav Ashok Kumar S. Sandeep Kumar Department of Information Technology St. Peter's Engineering College Hyderabad Telangana India Department of Computer Science and Engineering NPR College of Engineering & Technology Dindigul Tamil Nadu India Department of Electronics and Communication Engineering Government Women Engineering College Ajmer Rajasthan India Department of Mathematics SRM Institute of Science and Technology Ghaziabad Uttar Pradesh India Department of Computer Science BanasthaliVidyapith Rajasthan India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

In order to recognize patterns in images, this study tests the performance of many “machine learning algorithms” and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods (“deep learning, support vector machines, decision trees, and random forests”) and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). the efficacy of each algorithm is measured in terms of its “accuracy, precision, recall, and F1 score”, among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. the results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms.

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A robust classifier for Noise-corruption learning

A robust classifier for Noise-corruption Learning

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international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Linchang Zhao Hao Wei Mu Zhang Ruiping Li Qianbo Li Hongming Cai School of Computer Science Guiyang University Guiyang China School of Building Equipment Guizhou Polytechnic of Construction Guiyang China School of software Shanghai Jiao Tong University Shanghai China

Deep neural networks (DNNs) have achieved great success in various applications across many disciplines. However, their superior performance is susceptible to training set bias and noise label corruption. In order to overcome the overfitting of deep network models to corrupted labels, in addition to various regularization techniques and example re-weighting algorithms, teacher-student models based on the knowledge distillation paradigm are also a popular approach to solve these problems. However, these methods require careful tuning of additional hyperparameters, such as example mining plans and regularization hyperparameters. Unlike previous re-weighting methods, this paper proposes a novel meta-learning algorithm based on deep transfer hybrid model for training example weight allocation. the method combines the RN50STN model designed based on the spatial transformer network (STN) and pre-trained ResNet50, resulting in good performance in a wide range of noisy label corruption.

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